Methane is a potent greenhouse gas that has been responsible for around 25 % of global warming since the industrial revolution. Consequently identifying and mitigating methane emissions comprise an important step in combating the climate crisis. We develop a new deep learning model to automatically detect methane plumes from satellite images and demonstrate that this can be applied to monitor large methane emissions resulting from the oil and gas industry.

Currently, aerosol properties from spaceborne multi-angle polarimeter (MAP) instruments can only be retrieved in cloud-free areas or in areas where an aerosol layer is located above a cloud. Therefore, it is important to be able to identify cloud-free pixels for which an aerosol retrieval algorithm can provide meaningful output. The developed neural network cloud screening demonstrates that cloud masking for MAP aerosol retrieval can be based on the MAP measurements themselves.

The TROPESS CrIS single-pixel CO profile retrievals are compared to the MOPITT CO products in steps of adjusting them to the common a priori assumptions. The two data sets are found to agree within 5 %. We also demonstrated and analyzed the proper steps in evaluating GISS ModelE CO simulations using satellite CO retrieval products for the western US wildfire events in September 2020.

Methane is a potent greenhouse gas and a current high-priority target for short- to mid-term climate change mitigation. Detection of individual methane emitters from space has become possible in recent years, and the volume of data for this task has been rapidly growing, outpacing processing capabilities. We introduce an automated approach, U-Plume, which can detect and quantify emissions from individual methane sources in high-spatial-resolution satellite data. 

We described a modified lidar inversion algorithm to retrieve aerosol extinction and size distribution simultaneously from two-wavelength elastic lidar measurements. Its major advantage is that the lidar ratio of each layer is determined iteratively by a lidar ratio–Ångström exponent lookup table. The algorithm was applied to the Raman lidar and CALIOP measurements. The retrieved results by our method are in good agreement with those achieved by Raman method.

Using eddy-covariance systems deployed in three cities, we aimed to elucidate the sources of discrepancies in flux estimations from different software packages. One crucial finding is the impact of low-frequency spectral loss corrections on tall-tower flux estimations. Our findings emphasize the significance of a standardized measurement setup and consistent postprocessing configurations in minimizing the systematic flux uncertainty resulting from the usage of different software packages.

In the present study, a UAV platform with sensing and sampling systems was developed for 3D air pollutant concentration measurements. The sensing system of this platform contains multiple microsensors and IoT technologies for obtaining the real-time 3D distributions of critical air pollutants. The sampling system contains gas sampling sets and a 1 L Tedlar bag instead of a canister for the 3D measurement of VOC concentrations in accordance with the TO-15 method of the US EPA.

We have set up a measurement system for methane-clumped isotopologues. We have built an extraction and purification system to extract pure methane for these measurements, for samples of various origins, including atmospheric air, for which we need to process about 1000 L of air for one measurement. We report here the technical setup for extraction and measurements, as well as the calibration, and we give an overview of the samples measured so far.

We developed a novel instrument to determine the quality and number of biological and non-biological particles, with respect to their ice-promoting capacity as a function of temperature. The measurement uncertainty was determined, and the instrument produced reliable results. Further, repeated measurements of the same suspension showed that the instrument had high reproducibility.

Three-dimensional wind fields can be accurately measured by sonic anemometers. However, the traditional mast-mounted sonic anemometers are not flexible in various applications, which can be potentially overcome by drones. Therefore, we conducted a proof-of-concept study by applying three continuous-wave Doppler lidars to characterize the complex flow around a drone to validate the results obtained by CFD simulations. Both methods show good agreement, with a velocity difference of 0.1 m s^-1.

An unprecedented 3-year aerosol dataset was collected from a recent NASA field campaign over the western North Atlantic Ocean, which offers a special opportunity to evaluate two state-of-the-art remote sensing instruments, one lidar and the other polarimeter, on the same aircraft. Special attention has been paid to validate aerosol optical depth data and their uncertainties when no reference dataset is available.  Physical reasons for the disagreement between two instruments are discussed.

Transporting a representative aerosol sample to instrumentation inside a research aircraft remains a challenge due to losses or enhancements of particles in the aerosol sampling system. Here, we present sampling efficiencies and the cutoff diameter for the DLR Falcon aerosol sampling system as a function of true airspeed by comparing the in-cabin and the out-cabin particle number size distributions observed during the A-LIFE aircraft mission.

We analyze Landsat 8 (L8) and Sentinel-2B (S-2B) observations of the 2022 Nord Stream 2 methane leak and show how challenging this case is for usual data analysis methods. We provide customized calibrations for this Nord Stream 2 case and assess that no firm conclusion can be drawn from L8 or S-2B single overpasses. However, if we opportunistically assume that L8 and S-2B results are independent, we find an averaged L8 and S-2B combined methane leak rate of 502 ± 464 t h⁻¹.

This paper describes a new lidar system developed in Germany intended to study wind and temperature at night in the middle atmosphere. The paper explains how we have set up the system to work automatically and gives technical details for anyone who wants to build a similar system. We present a case study showing temperatures and winds at different altitudes. In a future article, we will present how we process the data and deal with uncertainties.

Microwave links, often part of mobile phone networks, can be used to measure rainfall along the link path by determining the signal loss caused by rainfall. We use high-frequency data of multiple microwave links to recreate commonly used sampling strategies. For time intervals up to 1 min, the influence of sampling strategies on estimated rainfall intensities is relatively little, while for intervals longer than 5–15 min, the sampling strategy can have significant influences on the estimates.

The use of uncrewed aircraft systems (UASs) to conduct a vertical profiling of ozone and meteorological variables was evaluated using comparisons between tower or ground observations and UAS-based measurements. Changes to the UAS profiler showed an improvement in performance. The profiler was used to see the impact of Chicago pollution plumes on a shoreline area near Lake Michigan.

We present global atmospheric methane (CH₄) and nitrous oxide (N₂O) distributions retrieved from measurements of the MIPAS instrument on board the Environmental Satellite (Envisat) during 2002 to 2012. Monitoring of these gases is of scientific interest because both of them are strong greenhouse gases. We analyze the latest, improved version of calibrated MIPAS measurements. Further, we apply a new retrieval scheme leading to an improved CH₄ and N₂O data product .

We present a new bromine monoxide (BrO) product derived using radiances measured from OMPS-NM on board the Suomi-NPP satellite. This product provides nearly a decade of global stratospheric and tropospheric column retrievals, a feature that is currently rare in publicly accessible datasets. Both stratospheric and tropospheric columns from OMPS-NM demonstrate robust performance, exhibiting good agreement with ground-based observations collected at three stations (Lauder, Utqiagvik, and Harestua).

Field comparison of dual-spot (AE33) and single-spot (AE31) Aethalometers by full-year collocated measurements suggests that site-specific correction factors are needed to ensure the long-term data continuity for AE31-to-AE33 transition in black carbon monitoring networks; b_abs agrees well between AE33 and AE31, with slight variations by wavelength (slope: 0.87–1.04; R²: 0.95–0.97). A ~ 20 % difference in secondary brown carbon light absorption was found between AE33 and AE31.

Using Gaofen-5B satellite data, this study detected 93 methane plume events from 32 coal mines in Shanxi, China, with emission rates spanning from 761.78 ± 185.00 to 12729.12 ± 4658.13 kg h^-1, showing significant variability among sources. This study highlights Gaofen-5B’s capacity for monitoring large methane point sources, offering valuable support in reducing greenhouse gas emissions.

To have better understanding of the dynamics of the lower and middle atmosphere, we installed a newly designed dual-frequency radar system that uses 53.8 MHz for near-ground to 20 km wind measurements and 35.0 MHz for 70 to 100 km wind measurements. The initial results show its good performance, along with the analysis of typical winter gravity wave activities.

We introduce a concept utilizing a differential absorption barometric lidar operating within the 1.96 µm CO₂ absorption band. Our focus is on a compact lidar configuration, featuring reduced telescope size and lower laser pulse energies towards minimizing costs for potential forthcoming Mars missions. The core measurement objectives encompass the determination of column CO₂ absorption optical depth and abundance, surface air pressure, and vertical distributions of dust and cloud layers.

We present an analysis from two large-scale mobile air quality monitoring campaigns in Colorado and California. We compare mobile measurements of air quality to measurements from nearby regulatory sites. The goal of this paper is to explore how fixed-site measurements (such as regulatory site measurements) can be used for ongoing instrument performance assessment of mobile monitoring platforms over extended measurement campaigns.

We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view.

The mixing of air in the lower atmosphere influences the concentration of air pollutants and greenhouse gases. Our study developed a new method, Deep-Pathfinder, to estimate mixing layer height. Deep-Pathfinder analyses imagery with aerosol observations using artificial intelligence techniques for computer vision. Compared to existing methods, it improves temporal consistency and resolution and can be used in real time, which is valuable for aviation, forecasting, and air quality monitoring.